Over the last decade, Amazon’s cloud ecosystem has grown at a breakneck pace and the need for a dedicated Amazon Redshift reporting tool has grown with it. Enterprises running Redshift at scale quickly discover that moving data into the warehouse is only half the problem; the other half is turning Redshift data into trustworthy reports that finance, operations, and executives can actually use. In 2019, AWS touched net sales of US $35 billion making it the clear leader in cloud computing. One of the key drivers of this growth has come from Amazon Redshift, the data warehouse product in the AWS ecosystem that is designed for fast performance and incredible scale.
From a business model perspective, several enterprises around the world opted for Amazon Redshift because it was a “data warehouse as a service” or DWaaS, with a pay-per-month business model. Additionally, Redshift takes advantage of its Massively Parallel Processing (MPP) capability which speeds up the processing of larger datasets for analytics. It also seamlessly integrates with AWS Machine Learning, a key requirement for the future as more enterprises embrace next-generation technologies like AI and machine learning.
At Orbit, we’ve worked with various enterprises that are leveraging emerging cloud-based data warehousing solutions like Amazon Athena, Redshift, Snowflake, and Google BigQuery. A common use case we observe is that cloud-based data warehouses are being used by companies to pull data from various sources – both on-prem and cloud. While legacy data continues to be on Oracle ERP or SAP, companies are looking to use a cloud-based data warehouse, especially for reporting, BI, and analytics.
In this context, Orbit’s BI, Reporting, and Analytics solutions are very relevant.Thanks to our data management and data modeling capabilities, coupled with the DataJump feature for data extracts, Orbit functions as a complete Amazon Redshift reporting tool — pulling from multiple sources, modeling the data, and delivering self-service Redshift reports and dashboards to end users.
We recently worked with a leading satellite television company to pull data from multiple data sources including Amazon Redshift and Athena. The company was also using Amazon Athena for running queries and analyzing big data on S3. The company evaluated various options for operational reporting and analytics and chose Orbit for a variety of reasons.
In this post, we share a few reasons why Orbit is probably the best solution in the market to harness the full power of data, garner intelligence and insights from data residing in cloud data warehouses like Amazon Redshift and Amazon Athena.
Unify Redshift Reports with Legacy Data Warehouses
Most enterprises around the world are now grappling with an explosion of data. A common scenario is that ERP data continues to reside in legacy data warehouses. However, to leverage the full potential of BI and analytics, companies are using Amazon Redshift and Amazon Athena because it offers one of the best integration capabilities with your data lake and AWS services, a lot of the maintenance is automated and therefore it is easy to manage, and it offers wonderful performance, security, and scalability.
With Orbit, it is easy to pull data from both legacy warehouses and Amazon Redshift and Amazon Athenaensures all Redshift reports and cross-source analytics are available in one place, a capability most AWS reporting tools lack out of the box.
Orbit: The All-in-One Amazon Redshift Reporting Tool
While AWS provides the underlying warehouse, most teams still need a dedicated Amazon Redshift reporting tool to turn Redshift data into finished reports, dashboards, and scheduled deliverables. Orbit Reporting and Analytics is that tool — a fully featured reporting platform purpose-built to sit on top of Redshift (and Athena) without forcing your team to stitch together QuickSight, SQL Workbench, and a scheduling script.
Visual Redshift reports and dashboards
Orbit ships with a drag-and-drop report designer and a visualization library covering tables, pivots, charts, gauges, and parameterized drill paths. Business users build Redshift reports against curated semantic models instead of writing SELECT statements, while analysts retain full SQL access via Orbit’s SQL DirectQuery mode for custom Redshift queries that need exact control over joins, window functions, or DISTKEY-aware filters. The result is a single authoring surface where both casual business consumers and technical analysts can produce Redshift-backed reports side by side.
Self-service ad-hoc querying on Redshift
Orbit’s self-service layer lets finance, operations, and ops analysts pivot Redshift data on demand — no ticket to IT, no exported CSV. Row-level security is enforced against Redshift user groups so a controller sees only her cost centers, even when the underlying report is shared company-wide. Users can save personal views, export to Excel or PDF, and subscribe colleagues to the output, which turns the same Redshift cluster into a shared analytical resource across departments without forcing every question back through a central BI team.
Scheduled distribution and drill-down
Orbit’s scheduler runs Redshift reports on daily, weekly, or monthly cadences and delivers them by email, SFTP, or to a shared portal — the workflow most AWS-native tools leave you to build yourself. Recipients can drill from summary numbers down to the individual Redshift transaction, then pivot into a linked report on Oracle EBS, NetSuite, or SAP without losing context. This closes the loop between automated delivery and interactive investigation, so a weekly ops pack or monthly finance deck becomes a starting point for drill-down rather than a static PDF.
Unified reporting across Redshift, Athena, and legacy ERPs
Most enterprises don’t run Redshift in isolation; finance still closes in Oracle EBS or Oracle Fusion, and supply chain data may live in SAP or S3/Athena. Orbit blends Redshift with those sources in a single report through Orbit Data Pipeline, so a month-end pack can combine GL balances from Oracle with Redshift-hosted operational data in one dashboard something native AWS reporting tools cannot do without significant custom engineering. For organizations mid-migration to Redshift, this also means analysts don’t have to wait for every historical source to land in the warehouse before producing unified reports.
Excel Edge and GLSense: Redshift Reporting Inside Excel
For business users, analysis is often done in Excel. The BI, reporting, and analytics tool must be capable of accessing real-time data from the operational databases and data warehouse, and most business intelligence solutions in the market don’t have capability to do this easily.
Self-Service Redshift Reporting Capabilities
Self-service is one of Orbit’s most sought-after capabilities that lets business users generate their reports in real-time and “on-demand,” without dependence on IT, to run analytics and speed up the decision-making process.
The scheduling feature as well as data visualization capabilities further provide users with flexibility and ease of use. This way, users can keep pace with business operations as Orbit enables keeping track of key business metrics and KPIs in real-time. Different queries can be posed to generate data summaries as required by the users.
The ability to access a wide range of dashboards and reports without having to write code is a key value proposition of Orbit.
A Stronger Alternative to Native AWS Reporting Tools
AWS does have a range of native AWS reporting tools: Amazon Kinesis for real-time analytics, Elasticsearch Service for operational analytics, QuickSight for dashboards and visualization, and AWS Deep Learning AMIs for predictive analytics. Orbit complements these by acting as the unified reporting layer most enterprises actually need. There is Amazon Kinesis for real-time analytics, Amazon Elasticsearch Service for operational analytics, Amazon QuickSight for dashboards and visualization, and AWS Deep Learning AMIs for predictive analytics using AI/ML.
But, for business users and business leaders, BI must be designed for ease-of-use and time savings. Also, the BI solutions must connect and integrate with ERPs and business systems that are not on the Amazon ecosystem.
Several enterprises choose Orbit for the depth and breadth of our product from three perspectives:
- Ease of integration with disparate data sources
- Unique self-service features and capabilities
- Range of pre-built content and reports
If you would like to talk to an expert or explore a case study of how we helped a Fortune 250 company with BI on top of Amazon Redshift and Amazon Athena, contact us here.
Frequently asked questions
Is Orbit a certified Amazon Redshift reporting tool?
Yes. Orbit connects to Amazon Redshift via native JDBC and supports the full Redshift SQL dialect, including DISTKEY and SORTKEY optimization. Orbit Reporting and Analytics has been deployed as the primary Amazon Redshift reporting tool at Fortune 250 accounts running multi-terabyte Redshift clusters alongside Amazon Athena and legacy ERPs.
How does Orbit compare to QuickSight for Redshift reports?
QuickSight is a solid dashboarding tool for AWS-native teams, but it was not built for pixel-perfect financial reports, complex scheduling workflows, or Excel-first analysts. Orbit adds full report designer capabilities, GLSense for Excel-native Redshift reporting, row-level security against non-AWS ERPs, and drill-through between Redshift and Oracle/SAP data — capabilities QuickSight does not offer natively.
Can I run Redshift reports directly in Excel?
Yes. Orbit’s Excel Edge and GLSense add-ins let finance and operations users query Redshift from within Excel, refresh live balances or transaction detail, and build pivot tables against Redshift data without exporting CSVs. This makes Orbit the only mainstream Amazon Redshift reporting tool with true Excel-native reporting.
Does Orbit support both Redshift and Athena in the same report?
Yes. Orbit Data Pipeline blends Amazon Redshift and Amazon Athena sources in a single report or dashboard, so teams can combine warehouse-grade Redshift data with S3/Athena data-lake queries without duplicating data. This is a common pattern for customers running Redshift for structured analytics and Athena for ad-hoc lake queries.
